Washington: When genes are turned on and expressed, they show patterns in cells that are similar in type and function in all tissues and organs. The discovery of these patterns improves our understanding of cells, which has implications for revealing disease mechanisms.
The advent of the spatial transcriptomics has allowed researchers to observe gene expression in its spatial context in whole tissue samples. But new computational methods are needed to make sense of these data and help identify and understand these patterns of gene expression.
An investigative team led by Jian Ma, the Lightning Y Stephanie Lane Professor of Computational Biology at Carnegie Mellon University’s School of Computer Science, he has developed a machine learning tool to fill this gap. His article on the method, called SPICE MIXappeared as a cover story in the most recent issue of Nature Genetics.
SPICEMIX helps researchers unravel the role that different spatial patterns play in the overall gene expression of cells in complex tissues such as the brain. It does this by representing each pattern with spatial metagenes, groups of genes that may be connected to a specific biological process and may show uniform or sporadic patterns across tissue.
The team, which included Ma; Benjamin Chidester, project scientist in the Department of Computational Biology; and doctoral students tianming zhou and Shahul Alam, used SPICEMIX to analyze spatial transcriptomics data from brain regions in mice and humans. They took advantage of the unique capabilities of SPICEMIX to uncover the landscape of cell types and spatial patterns in the brain.
“We were inspired by the kitchen when we chose the name,” Chidester said. “You can make all kinds of different flavors with the same set of spices. Cells can work in a similar way. They can use a common set of biological processes, but the specific combination they use gives them their unique identity.”
When applied to brain tissues, SPICEMIX identified spatial patterns of cell types in the brain with greater precision than other methods. He also discovered new expression patterns of brain cell types through learned spatial metagenes.
“These findings may help us paint a more complete picture of the complexity of brain cell types,” Zhou said.
The number of studies using spatial transcriptomics technologies is growing rapidly, and SPICEMIX can help researchers make the most of this high-volume, high-dimensional data.
“Our method has the potential to advance spatial transcriptomics research and contribute to a deeper understanding of both basic biology and disease progression in complex tissues,” Ma said.
